Adaptive neuro-fuzzy inference system (ANFIS) simulation for predicting overall acceptability of ice cream

Because of uncertain nature of sensory evaluation due to differences in the individual panelist's perception of the product attributes, application of fuzzy set concept could be useful. In this research, adaptive neuro-fuzzy inference system (ANFIS) was used to predict overall acceptability of ice cream. Consumer acceptance has been recognized as the key driver for product process. Experimental sensory attributes (flavor, body & texture, viscosity and smoothness) were used as inputs and independent overall acceptability as output of ANFIS. Thirty percent, thirty percent and forty percent of the sensory attributes data were used for training, checking and testing of the ANFIS model, respectively. It was found that ANFIS model achieved an average prediction error of overall acceptability of ice cream of only 5.11%. These results indicate that this model could potentially be used to estimate overall sensory acceptance of ice cream and related products.

[1]  S. Kupongsak,et al.  Application of fuzzy set and neural network techniques in determining food process control set points , 2006, Fuzzy Sets Syst..

[2]  J. Stokols,et al.  PROFILING OF SENSORY EVALUATION OF A NO-SUGAR-ADDED VANILLA ICE CREAM AMONG SPECIFIC AGE AND GENDER POPULATIONS , 2005 .

[3]  S. Razavi,et al.  Modeling of rheological behavior of honey using genetic algorithm–artificial neural network and adaptive neuro-fuzzy inference system , 2015 .

[4]  David Kilcast,et al.  Sensory perception of creaminess and its relationship with food structure , 2002 .

[5]  S. Arntfield,et al.  Textural analysis of fat reduced vanilla ice cream products , 2001 .

[6]  F. Salehi.  Current and future applications for nanofiltration technology in the food processing , 2014 .

[7]  A. Akesowan.  Influence of Soy Protein Isolate on Physical and Sensory Properties of Ice Cream , 2009 .

[8]  M. Samhouri,et al.  Fuzzy Identification and Modeling of a Gum-Protein Emulsifier in a Model Mayonnaise Color Development System , 2007 .

[9]  F. Salehi,et al.  Effect of Different Drying Methods on Rheological and Textural Properties of Balangu Seed Gum , 2014 .

[10]  Fakhreddin Salehi,et al.  Dynamic modeling of flux and total hydraulic resistance in nanofiltration treatment of regeneration waste brine using artificial neural networks , 2012 .

[11]  Georges Corrieu,et al.  Decision support system design using the operator skill to control cheese ripening––application of the fuzzy symbolic approach , 2004 .

[12]  C. Soukoulis,et al.  Study of the functionality of selected hydrocolloids and their blends with κ-carrageenan on storage quality of vanilla ice cream , 2008 .

[13]  Mostafa Mazaheri Tehrani,et al.  Application and Functions of Stabilizers in Ice Cream , 2011 .

[14]  Chris Clarke,et al.  The Science of Ice Cream , 2005 .

[15]  Steffen Becker,et al.  Predictive models for PEM-electrolyzer performance using adaptive neuro-fuzzy inference systems , 2010 .

[16]  M. A. Lazim,et al.  Sensory Evaluation of the Selected Coffee Products Using Fuzzy Approach , 2009 .

[17]  Fakhreddin Salehi,et al.  Modeling of waste brine nanofiltration process using artificial neural network and adaptive neuro-fuzzy inference system , 2016 .

[18]  Murad Samhouri,et al.  Formulation and fuzzy modeling of emulsion stability and viscosity of a gum–protein emulsifier in a model mayonnaise system , 2008 .

[19]  S. Jaya,et al.  SENSORY EVALUATION OF MANGO DRINKS USING FUZZY LOGIC , 2003 .

[20]  Taizo Hanai,et al.  Sensory modeling of coffee with a fuzzy neural network , 2002 .

[21]  Mohd Azlan Hussain,et al.  Thermal conductivity prediction of foods by Neural Network and Fuzzy (ANFIS) modeling techniques , 2012 .

[22]  V. Alvárez.  Ice Cream and Related Products , 2008 .

[23]  M. Al-Mahasneh,et al.  Modeling moisture sorption isotherms in roasted green wheat using least square regression and neural-fuzzy techniques , 2012 .

[24]  Fakhreddin Salehi,et al.  Predicting Total Acceptance of Ice Cream Using Artificial Neural Network , 2014 .

[25]  Juing-Shian Chiou,et al.  ANFIS based Controller Design for Biped Robots , 2007, 2007 IEEE International Conference on Mechatronics.

[26]  Gilles Trystram,et al.  Fuzzy concepts applied to food product quality control: A review , 2006, Fuzzy Sets Syst..

[27]  Olusegun Folorunso,et al.  Fuzzy-Rule-Based Approach for Modeling Sensory Acceptabitity of Food Products , 2009, Data Sci. J..

[28]  I. Ioannou,et al.  Fuzzy sausage drying control based on human sensory evaluations , 2004, Proceedings of the 21st IEEE Instrumentation and Measurement Technology Conference (IEEE Cat. No.04CH37510).

[29]  H. .,et al.  Effect of Skim Milk in Soymilk Blend on the Quality of Ice Cream , 2003 .

[30]  A. Riazi,et al.  A compositional study on two current types of salep in Iran and their rheological properties as a function of concentration and temperature , 2007 .

[31]  Wang Yongqiang,et al.  Road Safety Evaluation from Traffic Information Based on ANFIS , 2008, 2008 27th Chinese Control Conference.